Perform the following steps to classify the churn data with the Naïve Bayes classifier:

1. Load the e1071 library and employ the naiveBayes function to build the classifier:

        > library(e1071) 
        > classifier=naiveBayes(trainset[, !names(trainset)
        %in% c("churn")], trainset$churn)

2. Type classifier to examine the function call, a-priori probability, and conditional probability:

> classifier
Output
Naive Bayes Classifier for Discrete Predictors

Call:
naiveBayes.default(x = trainset[, !names(trainset) %in% c("churn")], 
    y = trainset$churn)

A-priori probabilities:
trainset$churn
      yes no 
0.1477322 0.8522678 

Conditional probabilities:
              international_plan
trainset$churn no yes
           yes 0.70467836 0.29532164
           no 0.93512418 0.06487582 

3. Next, you can generate a classification table for the testing dataset:

        > bayes.table = table(predict(classifier, testset[,
        !names(testset) %in% c("churn")]), testset$churn)
        > bayes.table
        Output
            yes no
         yes 68 45
         no 73 832

4. Lastly, you can generate a confusion matrix from the classification table:

> confusionMatrix(bayes.table)
Output
Confusion Matrix and Statistics


      yes no
  yes 68 45
  no 73 832

               Accuracy : 0.8841 
                 95% CI : (0.8628, 0.9031)
    No Information Rate : 0.8615 
   P-Value [Acc > NIR] : 0.01880 

                  Kappa : 0.4701 
 Mcnemar's Test P-Value : 0.01294 

            Sensitivity : 0.4823 
            Specificity : 0.9487 
         Pos Pred Value : 0.6018 
         Neg Pred Value : 0.9193 
             Prevalence : 0.1385 
         Detection Rate : 0.0668 
   Detection Prevalence : 0.1110 
      Balanced Accuracy : 0.7155 

       'Positive' Class : yes